As is well known, Multiuser detection (MUD) typically refers to the detection of data in non-orthogonal multiplexes, such as used to jointly demodulate co-channel interfering digital signals. MUD processing increases the number of information bits available per chip or signaling dimension for interference limited systems. Optimal MUD based on the maximum likelihood principle operates by comparing the received signal with the entire number of possibilities that may have occurred at the ensemble of transmitters to give rise to the waveform received at the receiver. The maximum likelihood detector is a brute force approach which requires an exhaustive search and consists of evaluating the Euclidean distance between the received samples and the linear model of the samples using every possible hypothesis of the bit sequence. For problems with a large number of users or severe intersymbol interference from multipaths, the maximum likelihood computations are complex and time-consuming, thus making real-time operation impractical or impossible.
Reduced complexity approaches based on conventional tree-pruning algorithms help to some extent, but the performance of such multiuser detection algorithms degrades as the parameter M (pruning factor) is decreased, since M governs the number of computations required. Thus, to combat “improper” pruning, basic tree-pruning must ensure that M is large enough so that an accurate result is generated. As a result, conventional pruning methods are still associated with increased complexity, particularly when the number of interfering signals is moderate to large.
And, the number of interfering signals continues to increase with advances in technology. Many communication systems today rely on multiple-access signal packing schemes where multiple signals occupy the same time and frequency channel. One major performance limitation in these types of systems is multiple-access interference where the correlation between the user's signature waveforms is non zero. In these cases, multiuser detection algorithms must be employed by the receiver to best mitigate this interference. Assuming the signal parameters for each user is known by the receiver, the maximum likelihood (ML) solution is to jointly search over every possible combination of each users transmit symbol sequence, then select the hypothesis that maximizes the likelihood function. As described, the problem with this approach is that it is computationally inefficient and may not be feasible for real time requirements.
Therefore, it is of interest to develop sub-optimal detectors that achieve near ML performance with less time and less complexity. One algorithm that somewhat reduces the complexity problem is the M-Algorithm, wherein only a fixed number of hypotheses, M, are tested based on interim metrics that are calculated at each stage in the decision tree to limit computation complexity. However, as noted herein, because the number of hypothesis to test each stage is fixed in the M-Algorithm, this technique by itself can still be inadequate, especially in a dynamic environment where the number of users and the interference environment is constantly changing.
What is needed therefore is a system and associated techniques for dynamically mitigating interference problems in a computationally efficient manner.